Suivi automatique de personnes en mouvement par analyse d'images couleurs successives. Application au suivi de joueurs de football.

Dans cet article, nous présentons une méthode de suivi de joueurs de football où les joueurs sont modélisés par des contours actifs évalués après classification couleur de chaque pixel. La position de chaque joueur extrait des images d'un match peut ainsi être déterminée sauf si ce joueur est caché par un adversaire. Dans ce cas, la reconnaissance de l'équipe des joueurs par classification des pixels permet le suivi automatique de chacun d'eux. Cet algorithme utilise un apprentissage supervisé basé sur une analyse colorimétrique des tenues des joueurs

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Représentation de la couleur en analyse d'images

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Segmentation d'images couleur par classification de pixels dans des espaces d'attributs colorimétriques adaptés (application à l'analyse d'images de football)

Dans le cadre de l'analyse d'images de football, nous proposons une methodologie originale de segmentation d'images couleur en regions qui exploite les proprietes colorimetriques des pixels pour extraire de l'image les joueurs a suivre. Les pixels de chaque image sont affectes a differentes classes selon qu'ils representent le terrain, un joueur de l'une des deux equipes, un des deux gardiens de but ou un arbitre en utilisant des methodes classiques de classification de donnees multidimensionnelles fondees sur un apprentissage supervise. La couleur de chaque pixel est usuellement representee sur la base des trois composantes trichromatiques rouge, verte et bleue, mais peut etre codee dans d'autres systemes de representation que nous avons regroupes par familles en fonction de leurs differentes proprietes. L'originalite de notre approche consiste a construire un espace couleur hybride en selectionnant les composantes couleur les mieux adaptees aux classes de pixels a retrouver et pouvant etre issues de differents systemes. Pour cela, nous utilisons une methode d'analyse discriminante associee a des criteres informationnels de discrimination. Cette approche est generalisee en considerant qu'un pixel est represente par des attributs colorimetriques evalues a son voisinage. Il est ainsi possible de proposer une liste d'attributs calcules pour chacune des composantes couleur des systemes de representation. Le voisinage dans lequel sont calcules ces attributs colorimetriques permet de definir une texture couleur et de restituer ainsi les relations de connexite entre les pixels voisins. Les attributs colorimetriques les plus discriminants sont regroupes au sein d'un espace d'attributs colorimetriques adapte a la classification.LILLE1-BU (590092102) / SudocSudocFranceF

Haralick feature extraction from LBP images for color texture classification

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Comparison of feature selection schemes for color texture classification

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A multi color space approach for texture classification: experiments with outex, vistex and barktex image databases

International audienceThe color of pixels can be represented in different color spaces which respect different properties. Many authors have compared the classification performances reached by these color spaces in order to determine the one which would be the well suited to color texture analysis. However, the synthesis of these works shows that the choice of the color space depends on the considered texture images. Moreover, the prior determination ofa color space which is well suited to the considered class discrimination is not easy. That is why we propose to consider a multicolor space approach designed for color texture classification. It consists in selecting, among a set of color texture features extracted from images coded in different color spaces, those which are the most discriminating for the considered color textures. In this paper, we experimentally study the contribution of this multicolor space with three well-known benchmark databases, namely Outex, Vistex and Barktex. Comparison and discussion are then carried out

Comparison of color imaging vs. hyperspectral imaging for texture classification

International audienceMany approaches of texture analysis by color or hyperspectral imaging are based on the assumption that the image of a texture can be viewed as a multi-component image, where spatial interactions within and between components are jointly considered (opponent component approach) or not (marginal approach). When color images are coded in multiple color spaces, texture descriptors are based on Multi Color Channel (MCC) representations. By extension, a Multi Spectral Band (MSB) representation can be used to characterize the texture of material surfaces in hyperspectral images. MSB and MCC representations are compared in this paper for texture classification issues. The contribution of each representationis investigated with marginal and/or opponent component strategies. For this purpose, several relevant texture descriptors are considered. Since MSB and MCC representations generate high-dimensional feature spaces, a dimensionality reduction is applied to avoid the curse of dimensionality. Experimental results carried out on three hyperspectral texture databases (HyTexiLa, SpecTex and an original dataset extracted from the Timbers database) show that considering between component interactions in addition to the within ones significantly improves the classification accuracies. The proposed approaches allow also to outperform state of the art hand-designed descriptors and color texture descriptors based on deep learning networks. This study highlights the contribution of hyperspectral imaging compared to color imaging for texture classification purposes but also the advantages of color imaging depending on the considered texture representatio